The major purpose of OLAP is decision support. Users can gain insights into what is actually driving their business. They can measure effectiveness, maximize the ROI (return on investment) and thus maintain competitive advantage. OLAP is a key component to an organization's business intelligence infrastructure.
OLAP Servers make it easy to get summarized information. OLAP Servers aggregate data at build time and store these aggregations. Aggregations can use a number of different built-in OLAP statistics to aggregate data. Statistics are stored with OLAP cubes and do not require additional computation when accessed by queries. The short response times of OLAP data sources enable workers to navigate through data following their own train of thought. OLAP clients support the standard OLAP navigations such as:                Drilling up and down hierarchies.        Expanding and collapsing levels within hierarchies.        Slicing and dicing through data.        Drilling beyond the cube data into the underlying detailed data.        
It is possible to begin exploring the data by getting a high-level overview on corporate operations. Based on this view, OLAP technology enables fast and consistent drill down access into the details that describe the accumulated business metrics in order to pinpoint key elements and to view business activities from a number of different perspectives. OLAP allows users to be able to analyze data across any dimension, at different levels of aggregation, with equal functionality and ease.
Within the business domain using OLAP technology, there are varying levels of sophistication. Some people use OLAP without actually knowing that it is OLAP, simply by opening up reports based on OLAP data and exploring one of the other dimensions by drilling up and down. At the other extreme, there are business analysts who use OLAP data sources as a launch pad to exploring multivariate problems, possibly applying statistical procedures or predictive models to specific slices of data taken from an OLAP data source. Reports using OLAP data are likely to be relevant for a large audience, while only a few people in an enterprise might be knowledgeable about statistical procedures or predictive analytics.
These experts define analysis rules to calculate performance metrics from detailed business transaction data, and context rules to enable performance metrics to be tied to business goals and forecasts. Some basic automation can be achieved by applying exception rules to metrics and sending an alert to a business user when a metric exceeds a threshold defined in the exception rule.
There is need for a tool that will enable the non-specialized user to define a powerful exception rule on his data, without having expert knowledge of the database structure and without the need to perform any programming.